docstr: Online maximum Mean Discrepancy (MMD) data drift detector using preconfigured thresholds.
  
  Parameters
  ----------
  x_ref
      Data used as reference distribution.
  ert
      The expected run-time (ERT) in the absence of drift. For the multivariate detectors, the ERT is defined
      as the expected run-time from t=0.
  window_size
      The size of the sliding test-window used to compute the test-statistic.
      Smaller windows focus on responding quickly to severe drift, larger windows focus on
      ability to detect slight drift.
  backend
      Backend used for the MMD implementation and configuration.
  preprocess_fn
      Function to preprocess the data before computing the data drift metrics.
  x_ref_preprocessed
      Whether the given reference data `x_ref` has been preprocessed yet. If `x_ref_preprocessed=True`, only
      the test data `x` will be preprocessed at prediction time. If `x_ref_preprocessed=False`, the reference
      data will also be preprocessed.
  kernel
      Kernel used for the MMD computation, defaults to Gaussian RBF kernel.
  sigma
      Optionally set the GaussianRBF kernel bandwidth. Can also pass multiple bandwidth values as an array.
      The kernel evaluation is then averaged over those bandwidths. If `sigma` is not specified, the 'median
      heuristic' is adopted whereby `sigma` is set as the median pairwise distance between reference samples.
  n_bootstraps
      The number of bootstrap simulations used to configure the thresholds. The larger this is the
      more accurately the desired ERT will be targeted. Should ideally be at least an order of magnitude
      larger than the ERT.
  device
      Device type used. The default tries to use the GPU and falls back on CPU if needed.
      Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
      ``torch.device``. Only relevant for 'pytorch' backend.
  verbose
      Whether or not to print progress during configuration.
  input_shape
      Shape of input data.
  data_type
      Optionally specify the data type (tabular, image or time-series). Added to metadata.
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code: def __init__(
      self,
      x_ref: Union[np.ndarray, list],
      ert: float,
      window_size: int,
      backend: str = "tensorflow",
      preprocess_fn: Optional[Callable] = None,
      x_ref_preprocessed: bool = False,
      kernel: Optional[Callable] = None,
      sigma: Optional[np.ndarray] = None,
      n_bootstraps: int = 1000,
      device: TorchDeviceType = None,
      verbose: bool = True,
      input_shape: Optional[tuple] = None,
      data_type: Optional[str] = None,
  ) -> None:
      """
      Online maximum Mean Discrepancy (MMD) data drift detector using preconfigured thresholds.
  
      Parameters
      ----------
      x_ref
          Data used as reference distribution.
      ert
          The expected run-time (ERT) in the absence of drift. For the multivariate detectors, the ERT is defined
          as the expected run-time from t=0.
      window_size
          The size of the sliding test-window used to compute the test-statistic.
          Smaller windows focus on responding quickly to severe drift, larger windows focus on
          ability to detect slight drift.
      backend
          Backend used for the MMD implementation and configuration.
      preprocess_fn
          Function to preprocess the data before computing the data drift metrics.
      x_ref_preprocessed
          Whether the given reference data `x_ref` has been preprocessed yet. If `x_ref_preprocessed=True`, only
          the test data `x` will be preprocessed at prediction time. If `x_ref_preprocessed=False`, the reference
          data will also be preprocessed.
      kernel
          Kernel used for the MMD computation, defaults to Gaussian RBF kernel.
      sigma
          Optionally set the GaussianRBF kernel bandwidth. Can also pass multiple bandwidth values as an array.
          The kernel evaluation is then averaged over those bandwidths. If `sigma` is not specified, the 'median
          heuristic' is adopted whereby `sigma` is set as the median pairwise distance between reference samples.
      n_bootstraps
          The number of bootstrap simulations used to configure the thresholds. The larger this is the
          more accurately the desired ERT will be targeted. Should ideally be at least an order of magnitude
          larger than the ERT.
      device
          Device type used. The default tries to use the GPU and falls back on CPU if needed.
          Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
          ``torch.device``. Only relevant for 'pytorch' backend.
      verbose
          Whether or not to print progress during configuration.
      input_shape
          Shape of input data.
      data_type
          Optionally specify the data type (tabular, image or time-series). Added to metadata.
      """
      super().__init__()
  
      # Set config
      self._set_config(locals())
  
      backend = backend.lower()
      BackendValidator(
          backend_options={
              Framework.TENSORFLOW: [Framework.TENSORFLOW],
              Framework.PYTORCH: [Framework.PYTORCH],
          },
          construct_name=self.__class__.__name__,
      ).verify_backend(backend)
  
      kwargs = locals()
      args = [kwargs["x_ref"], kwargs["ert"], kwargs["window_size"]]
      pop_kwargs = [
          "self",
          "x_ref",
          "ert",
          "window_size",
          "backend",
          "__class__",
      ]
      [kwargs.pop(k, None) for k in pop_kwargs]
  
      if kernel is None:
          if backend == Framework.TENSORFLOW:
              from alibi_detect.utils.tensorflow.kernels import (
                  GaussianRBF,
              )
          else:
              from alibi_detect.utils.pytorch.kernels import GaussianRBF  # type: ignore
          kwargs.update({"kernel": GaussianRBF})
  
      if backend == Framework.TENSORFLOW:
          kwargs.pop("device", None)
          self._detector = MMDDriftOnlineTF(*args, **kwargs)
      else:
          self._detector = MMDDriftOnlineTorch(*args, **kwargs)  # type: ignore
      self.meta = self._detector.meta
  
file_path: alibi-detect/alibi_detect/cd/mmd_online.py
imports: import os
  import numpy
  from typing import Any, Callable, Dict, Optional, Union
  from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, BackendValidator, Framework
  from alibi_detect.base import DriftConfigMixin
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 1 to create a new MMDDriftOnline object from inherited base classes: DriftConfigMixin with example: obj = MMDDriftOnline(x_ref, ert, window_size, backend, preprocess_fn, x_ref_preprocessed, kernel, sigma, n_bootstraps, device, verbose, input_shape, data_type)